import os
import re
import lz4
import time
import uuid
import vllm
import torch
import spacy
import base64
import msgpack
import sqlite3
import outlines
import validators
import numpy as np
import pandas as pd
import streamlit as st
from numpy import ndarray
from datetime import datetime
from typing import List, Dict
from ppt_chunker import ppt_chunk
from outlines import models, generate
from qdrant_client import QdrantClient
from optimum_encoder import OptimumEncoder
from unstructured.cleaners.core import clean
from streamlit_navigation_bar import st_navbar
from vllm.sampling_params import SamplingParams
from fastembed import SparseTextEmbedding, SparseEmbedding
from unstructured.nlp.tokenize import download_nltk_packages
from huggingface_hub import snapshot_download, hf_hub_download
from scipy.sparse import csr_matrix, save_npz, load_npz, vstack
from langchain_experimental.text_splitter import SemanticChunker
from langchain_community.document_loaders import WikipediaLoader, WebBaseLoader
from sqlalchemy import (
    create_engine,
    MetaData,
    Table,
    Column,
    String,
    Integer,
    select,
    column,
)
from qdrant_client.models import (
    NamedSparseVector,
    NamedVector,
    SparseVector,
    PointStruct,
    ScoredPoint,
    Prefetch,
    FusionQuery,
    Fusion,
    SearchRequest,
    Modifier,
    OptimizersConfigDiff,
    HnswConfigDiff,
    Distance,
    VectorParams,
    SparseVectorParams,
    SparseIndexParams,
    Batch,
    PointIdsList,
    QueryRequest,
    Filter,
    HasIdCondition,
    Datatype,
    BinaryQuantization,
    BinaryQuantizationConfig,
    MultiVectorConfig
)

icon_to_types = {
    'ppt':('data:image/png;base64,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',
          'Powerpoint'),
    'pptx':('data:image/png;base64,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',
          'Powerpoint'),
    'txt':('data:image/png;base64,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',
          'Txt'),
    'doc':('data:image/png;base64,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',
          'Microsoft Word'),
    'docx':('data:image/png;base64,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',
          'Microsoft Word'),
    'xslx':('data:image/png;base64,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',
           'Excel')
}


def transform_query(query: str) -> str:
    """ For retrieval, add the prompt for query (not for documents).
    """
    return f'Represent this sentence for searching relevant passages: {query}'

def query_hybrid_search(query: str, client: QdrantClient, collection_name: str, dense_model: OptimumEncoder, sparse_model: SparseTextEmbedding):
    dense_embeddings = dense_model.embed_query(transform_query(query))[0]
    sparse_embeddings = list(sparse_model.query_embed(query))[0]

    return client.query_points(
        collection_name=collection_name,
        prefetch=[
            Prefetch(query=sparse_embeddings.as_object(), using="text-sparse", limit=25),
            Prefetch(query=dense_embeddings, using="text-dense", limit=25)
        ],
        query=FusionQuery(fusion=Fusion.RRF),
        with_vectors=False,
        with_payload=True,
        limit=10,
        score_threshold=0.8
    )
    
@outlines.prompt
def build_initial_prompt(query: str):
    """Determine whether the following query is a 'Domain-Specific Question' or a 'General Question.'
    
    A 'Domain-Specific Question' requires knowledge or familiarity with a particular field, niche, or specialized area of interest, including specific video games, movies, books, academic disciplines, or professional fields.
    A 'General Question' can be answered by a broad audience without requiring specific knowledge about a particular topic, franchise, or specialized interest.
    
    Query: {{ query }}
    
    Choose one: 'Domain-Specific Question' or 'General Question'
    """

@outlines.prompt
def open_query_prompt(past_messages: str, query: str):
    """{{ past_messages }}
    
    user: {{ query }}
    assistant:
    """

@outlines.prompt
def route_llm(context: str, query: str):
    """Understand the context and answer the question by 'Yes' or 'No'

    Context: {{ context }}

    Question: {{ query }}
    """

@outlines.prompt
def answer_with_context(context: str, query: str):
    """You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. Use three sentences maximum and keep the answer concise.
    
    Context: {{ context }} 
    
    Question: {{ query }} 
            
    Answer:
    """

@outlines.prompt
def idk(query: str):
    """Just say that you don't know have the knowledge to answer the following question

    Question: {{ query }}
    """

@outlines.prompt
def self_knowledge(query: str):
    """Answer the following question by using your own knowledge about the topic.

    Question: {{ query }}
    """

def main(query: str, client: QdrantClient, collection_name: str, llm, dense_model: OptimumEncoder, sparse_model: SparseTextEmbedding, past_messages: str):
    prompt = build_initial_prompt(query)
    gen_text = outlines.generate.text(llm)
    
    gen_choice = outlines.generate.choice(llm, choices=['Domain-Specific Question', 'General Question'])
    action = gen_choice(prompt, max_tokens=128, sampling_params=SamplingParams(temperature=0))
    print(f'Choice: {action}')
    
    if action == 'General Question':
        prompt = open_query_prompt(past_messages, query)
        answer = gen_text(prompt, max_tokens=300, sampling_params=SamplingParams(temperature=0.3))
    else:
        scored_points = query_hybrid_search(query, client, collection_name, dense_model, sparse_model).points
        
        docs = [(scored_point.payload['text'], scored_point.payload['metadata']) for scored_point in scored_points]
        contents, metadatas = [list(t) for t in zip(*docs)]
        
        context = "\n".join(contents)
        
        gen_choice = outlines.generate.choice(llm, choices=['Yes', 'No'])
        prompt = route_llm(context, 'Is the context relevant to the question ?')
        action = gen_choice(prompt, max_tokens=128, sampling_params=SamplingParams(temperature=0))

        if action == 'Yes':
            seen_values = set()
            result_metadatas = "\n\n".join(
                f'{value}'
                for metadata in metadatas
                for key, value in metadata.items()
                if (value not in seen_values and not seen_values.add(value))
            )
            
            prompt = answer_with_context(context, query)
            answer = gen_text(prompt, max_tokens=500, sampling_params=SamplingParams(temperature=0.3))
            answer = f"{answer}\n\n\nSource(s) :\n\n{result_metadatas}"
            
            if not st.session_state.documents_only:
                answer = f'Documents Based :\n\n{answer}'
        else:
            if st.session_state.documents_only:
                prompt = idk(query)
                answer = gen_text(prompt, max_tokens=500, sampling_params=SamplingParams(temperature=0.3))
            else:
                prompt = self_knowledge(query)
                answer = gen_text(prompt, max_tokens=500, sampling_params=SamplingParams(temperature=0.3))
                answer = f'Internal Knowledge :\n\n{answer}'

    return answer

def collect_files(conn, cursor, directory, pattern):
    array = []

    for filename in os.listdir(directory):
        if pattern in filename:
            if filename.endswith('.msgpack'):
                with open(os.path.join(directory, filename), "rb") as data_file_payload:
                    decompressed_payload = data_file_payload.read()
                    
                array.extend(msgpack.unpackb(decompressed_payload, raw=False))
            elif (filename.endswith('.npz')) and (pattern == '_dense'):
                array.extend(list(np.load(os.path.join(directory, filename)).values()))
            elif (filename.endswith('.npz')) and (pattern == '_sparse'):
                sparse_embeddings = []
                loaded_sparse_matrix = load_npz(os.path.join(directory, filename))
                
                for i in range(loaded_sparse_matrix.shape[0]):
                    row = loaded_sparse_matrix.getrow(i)
                    values = row.data.tolist()
                    indices = row.indices.tolist()
                    embedding = SparseVector(indices=indices, values=values)
                    sparse_embeddings.append(embedding)
                array.extend(sparse_embeddings)
            elif (filename.endswith('.npy')):
                ids_list = np.load(os.path.join(directory, filename), allow_pickle=True).tolist()
                insert_data(conn, cursor, os.path.splitext(filename)[0], ids_list)
                array.extend(ids_list)
                
    return array

def int_to_bytes(value):
    return base64.b64encode(str(value).encode())

def bytes_to_int(value):
    return int(base64.b64decode(value).decode())
    
def insert_data(conn, cursor, name, ids_array):
    cursor.execute('INSERT INTO table_names (doc_name) VALUES (?)', (name,))
    for ids in ids_array:
        cursor.execute('INSERT INTO table_ids (name, ids_value) VALUES (?, ?)', (name, int_to_bytes(ids)))
    conn.commit()
    
def retrieve_ids_value(conn, cursor, name):
    cursor.execute('SELECT ids_value FROM table_ids WHERE name = ?', (name,))
    rows = cursor.fetchall()
    return [bytes_to_int(row[0]) for row in rows]

def delete_document(conn, cursor, name):
    conn.execute('BEGIN')

    try:
        cursor.execute('DELETE FROM table_ids WHERE name = ?', (name,))
        cursor.execute('DELETE FROM table_names WHERE doc_name = ?', (name,))
        
        conn.commit()
        print(f"Deleted document '{name}' and its associated IDs.")
    except sqlite3.Error as e:
        conn.rollback()
        print(f"An error occurred: {e}")

@st.cache_resource(show_spinner=False)
def load_models_and_documents():
    container = st.empty()
    
    with container.status("Load AI Models and Prepare Documents...", expanded=True) as status:
        st.write('Downloading and Loading MixedBread Mxbai Dense Embedding Model under ONNX with Nvidia CUDA as backend...')

        dense_model = OptimumEncoder(
            device="cuda",
            cache_dir=os.getenv('HF_HOME')
        )

        st.write('Downloading and Loading Qdrant BM42 Sparse Embedding Model under ONNX using the CPU...')
        
        sparse_model = SparseTextEmbedding(
            'Qdrant/bm42-all-minilm-l6-v2-attentions',
            cache_dir=os.getenv('HF_HOME'),
            providers=['CPUExecutionProvider']
        )

        st.write('Downloading Mistral Nemo AI Model...')
        
        model_path = snapshot_download('casperhansen/mistral-nemo-instruct-2407-awq')

        st.write('Loading Mistral Nemo AI Model quantized with AWQ and using Outlines + vLLM Engine as backend...')

        llm = vllm.LLM(
            model=model_path,
            tokenizer=model_path,
            tensor_parallel_size=1,
            trust_remote_code=True,
            enforce_eager=True,
            quantization="awq",
            gpu_memory_utilization=1,
            max_model_len=12288,
            dtype=torch.float16
        )
        model = models.VLLM(llm)

        st.write('Loading Spacy Natural Language Processing Model for English...')
        nlp = spacy.load("en_core_web_sm")

        download_nltk_packages()

        st.write('Creating Collection for our Qdrant Vector Database in RAM memory...')
        
        client = QdrantClient(':memory:')
        collection_name = 'collection_demo'
        
        client.create_collection(
            collection_name,
            {
                'text-dense': VectorParams(
                    size=1024,
                    distance=Distance.COSINE,
                    datatype=Datatype.FLOAT16,
                    on_disk=False
                )
            },
            {
                'text-sparse': SparseVectorParams(
                    index=SparseIndexParams(
                        on_disk=False
                    ),
                    modifier=Modifier.IDF
                )
            },
            2,
            optimizers_config=OptimizersConfigDiff(
                indexing_threshold=0,
                default_segment_number=4
            ),
            hnsw_config=HnswConfigDiff(
                on_disk=False, 
                m=32,
                ef_construct=200
            )
        )

        conn = sqlite3.connect(':memory:', check_same_thread=False)
        conn.execute('PRAGMA foreign_keys = ON;')
        cursor = conn.cursor()

        cursor.execute('''
        CREATE TABLE table_names (
        doc_name TEXT PRIMARY KEY
        )
        ''')
        cursor.execute('''
        CREATE TABLE table_ids (
        name TEXT,
        ids_value BLOB,
        FOREIGN KEY(name) REFERENCES table_names(doc_name)
        )
        ''')

        cursor.execute('SELECT COUNT(*) FROM table_names')
        count = cursor.fetchone()[0]
        print(f'Is the table empty? {"Yes" if count == 0 else "No"}')
        
        name = 'action_rpg'
        embeddings_path = os.path.join(os.getenv('HF_HOME'), 'embeddings')

        payload_path = os.path.join(embeddings_path, name + '_payload.msgpack')
        dense_path = os.path.join(embeddings_path, name + '_dense.npz')
        sparse_path = os.path.join(embeddings_path, name + '_sparse.npz')
        ids_path = os.path.join(embeddings_path, name + '_ids.npy')

        if not os.path.exists(embeddings_path):
            os.mkdir(embeddings_path)

            st.write('Downloading and Loading Video Games Dataset coming from Wikipedia...')

            docs_1 = WikipediaLoader(query='Action-RPG').load()
            docs_2 = WikipediaLoader(query='Real-time strategy').load()
            docs_3 = WikipediaLoader(query='First-person shooter').load()
            docs_4 = WikipediaLoader(query='Multiplayer online battle arena').load()
            docs_5 = WikipediaLoader(query='List of video game genres').load()
            docs = docs_1 + docs_2 + docs_3 + docs_4 + docs_5

            texts, metadatas = [], []
            for doc in docs:
                texts.append(doc.page_content)
                del doc.metadata['title']
                del doc.metadata['summary']
                metadatas.append(doc.metadata)

            st.write('Transforming the Wikipedia Video Games Dataset into ingestable format for our Qdrant Vector Database...')
            
            payload_docs, dense_embeddings, sparse_embeddings = chunk_documents(texts, metadatas, dense_model, sparse_model)

            st.write('Saving on disk the Wikipedia Video Games Dataset into quickly ingestable format...')

            with open(payload_path, "wb") as outfile_texts:
                packed_payload = msgpack.packb(payload_docs, use_bin_type=True)
                outfile_texts.write(packed_payload)
            
            np.savez_compressed(dense_path, *dense_embeddings)
            max_index = 0
            for embedding in sparse_embeddings:
                if len(embedding.indices) > 0:
                    max_index = max(max_index, max(embedding.indices))
        
            sparse_matrices = []
            for embedding in sparse_embeddings:
                data = np.array(embedding.values)
                indices = np.array(embedding.indices)
                indptr = np.array([0, len(data)])
                matrix = csr_matrix((data, indices, indptr), shape=(1, max_index + 1))
                sparse_matrices.append(matrix)
            
            combined_sparse_matrix = vstack(sparse_matrices)
            save_npz(sparse_path, combined_sparse_matrix)

            unique_ids = []
            while len(unique_ids) < len(payload_docs):
                new_id = uuid.uuid4()
                while new_id.hex[0] == '0':
                    new_id = uuid.uuid4()
                unique_ids.append(new_id.int)
            
            insert_data(conn, cursor, name, unique_ids)
            
            np.save(ids_path, np.array(unique_ids), allow_pickle=True)
        else:
            st.write('Loading the saved documents on disk')

            patterns = ['_ids', '_payload', '_dense', '_sparse']

            unique_ids, payload_docs, dense_embeddings, sparse_embeddings = [
                collect_files(conn, cursor, embeddings_path, pattern) for pattern in patterns
            ]

        st.write('Ingesting saved documents on disk into our Qdrant Vector Database...')

        print(f'LEN FIRST : {len(unique_ids)}, {len(payload_docs)}, {len(dense_embeddings)}, {len(sparse_embeddings)}')

        client.upsert(
            collection_name,
            points=Batch(
                ids=unique_ids,
                payloads=payload_docs,
                vectors={
                    'text-dense': dense_embeddings,
                    'text-sparse': sparse_embeddings
                }
            )
        )
        
        client.update_collection(
            collection_name=collection_name,
            optimizer_config=OptimizersConfigDiff(indexing_threshold=20000)
        )
        status.update(
            label="Processing Complete!", state="complete", expanded=False
        )
        
    time.sleep(5)
    container.empty()
    
    return client, collection_name, model, dense_model, sparse_model, nlp, conn, cursor

def chunk_documents(texts: List[str], metadatas: List[dict], dense_model: OptimumEncoder, sparse_model: SparseTextEmbedding):
    text_splitter = SemanticChunker(
        dense_model,
        breakpoint_threshold_type='standard_deviation'
    )
    docs = text_splitter.create_documents(texts, metadatas)

    payload_docs, documents = [], []

    for doc in docs:
        payload_docs.append({ 'text': doc.page_content, 'metadata': doc.metadata })
        documents.append(doc.page_content)

    start_dense = time.time()
    dense_embeddings = dense_model.embed_documents(documents)
    end_dense = time.time()
    final_dense = end_dense - start_dense
    print(f'DENSE TIME: {final_dense}')

    start_sparse = time.time()

    sparse_embeddings = [SparseVector(indices=s.indices.tolist(), values=s.values.tolist()) for s in sparse_model.embed(documents, 32)]

    end_sparse = time.time()
    final_sparse = end_sparse - start_sparse
    print(f'SPARSE TIME: {final_sparse}')
    
    return payload_docs, dense_embeddings, sparse_embeddings

def on_change_documents_only():
    if st.session_state.documents_only:
        st.session_state.toggle_docs = {
            'tooltip': 'The AI answer your questions only considering the documents provided',
            'display': True
        }
    else:
        st.session_state.toggle_docs = {
            'tooltip': """The AI answer your questions considering the documents provided, and if it doesn't found the answer in them, try to find in its own internal knowledge""",
            'display': False
        }
    
    
if __name__ == '__main__':
    st.set_page_config(page_title="Multipurpose AI Agent",layout="wide", initial_sidebar_state='auto')

    client, collection_name, llm, dense_model, sparse_model, nlp, conn, cursor = load_models_and_documents()

    styles = {
        "nav": {
            "background-color": "rgb(204, 200, 194)",
        },
        "div": {
            "max-width": "32rem",
        },
        "span": {
            "border-radius": "0.5rem",
            "color": "rgb(125, 102, 84)",
            "margin": "0 0.125rem",
            "padding": "0.4375rem 0.625rem",
        },
        "active": {
            "background-color": "rgba(255, 255, 255, 0.25)",
        },
        "hover": {
            "background-color": "rgba(255, 255, 255, 0.35)",
        },
    }

    if 'menu_id' not in st.session_state:
        st.session_state.menu_id = 'ChatBot'
    
    st.session_state.menu_id = st_navbar(
        ['ChatBot', 'Documents'], 
        st.session_state.menu_id,
        options={
            'hide_nav': False,
            'fix_shadow': False,
            'use_padding': False
        },
        styles=styles
    )

    st.title('Multipurpose AI Agent')
    #st.markdown("<h1 style='position: fixed; top: 0; left: 0; width: 100%; padding: 10px; text-align: left; color: black;'>Multipurpose AI Agent</h1>", unsafe_allow_html=True)

    data_editor_path = os.path.join(os.getenv('HF_HOME'), 'documents')
    
    if 'df' not in st.session_state:
        if os.path.exists(data_editor_path):
            st.session_state.df = pd.read_parquet(os.path.join(data_editor_path, 'data_editor.parquet.sz'), engine='pyarrow')
        else:
            st.session_state.df = pd.DataFrame()
            os.mkdir(data_editor_path)

    if 'filter_ids' not in st.session_state:
        st.session_state.filter_ids = []

    def on_change_data_editor(conn, cursor, client, collection_name):
        print(f'Check : {st.session_state.key_data_editor}')
        
        if st.session_state.key_data_editor['deleted_rows']:
            st.toast('Wait for deletion to complete...')
            embeddings_path = os.path.join(os.getenv('HF_HOME'), 'embeddings')
            
            for deleted_idx in st.session_state.key_data_editor['deleted_rows']:
                name = st.session_state.df.loc[deleted_idx, 'document']
                print(f'WHAT IS THAT : {name}')
                os.remove(os.path.join(embeddings_path, name + '_ids.npy'))
                ids_values = retrieve_ids_value(conn, cursor, name)
                
                client.delete(
                    collection_name=collection_name, 
                    points_selector=PointIdsList(points=ids_values)
                )
                delete_document(conn, cursor, name)
                st.session_state.df.drop(deleted_idx)
                
            st.toast('Deletion Completed !', icon='🎉')
        elif st.session_state.key_data_editor['edited_rows']:
            edit_dict = st.session_state.key_data_editor['edited_rows']
            for key, value in edit_dict.items():
                toggle = value['toggle']
                st.session_state.df.loc[key, 'toggle'] = toggle
                retrieved_ids = retrieve_ids_value(conn, cursor, st.session_state.df.loc[key, 'document'])

                if not toggle:
                    st.session_state.filter_ids.extend(retrieved_ids)
                else:
                    st.session_state.filter_ids = [i for i in st.session_state.filter_ids if i not in retrieved_ids]
    
    if st.session_state.menu_id == 'Documents':       
        st.session_state.df = st.data_editor(
            st.session_state.df, 
            num_rows="dynamic",
            use_container_width=True,
            hide_index=True,
            on_change=on_change_data_editor,
            args=(conn, cursor, client, collection_name),
            key='key_data_editor',
            column_config={
                'icon': st.column_config.ImageColumn(
                    'Document'
                ),
                "document": st.column_config.TextColumn(
                    "Name",
                    help="Name of the document",
                    required=True
                ),
                "type": st.column_config.SelectboxColumn(
                    'File type',
                    help='The file format extension of this document',
                    required=True,
                    options=[
                        'Powerpoint',
                        'Microsoft Word',
                        'Excel'
                    ]
                ),
                "path": st.column_config.TextColumn(
                    'Path',
                    help='Path to the document',
                    required=False
                ),
                "time": st.column_config.DatetimeColumn(
                    'Date and hour',
                    help='When this document has been ingested here for the last time',
                    format="D MMM YYYY, h:mm a",
                    required=True
                ),
                "toggle": st.column_config.CheckboxColumn(
                    'Enable/Disable',
                    help='Either to enable or disable the ability for the ai to find this document',
                    required=True,
                    default=True
                )
            }
        )

    conversations_path = os.path.join(os.getenv('HF_HOME'), 'conversations')
    try:
        with open(conversations_path, 'rb') as fp:
            packed_bytes = fp.read()
        conversations: Dict[str, list] = msgpack.unpackb(packed_bytes, raw=False)
    except:
        conversations = {}

    if st.session_state.menu_id == 'ChatBot':  
        if 'id_chat' not in st.session_state:
            st.session_state.id_chat = 'New Conversation'

        def options_list(conversations: Dict[str, list]):
            if st.session_state.id_chat == 'New Conversation':
                return [st.session_state.id_chat] + list(conversations.keys())
            else:
                return ['New Conversation'] + list(conversations.keys())
        
        with st.sidebar:
            st.session_state.id_chat = st.selectbox(
                label='Choose a conversation',
                options=options_list(conversations),
                index=0,
                placeholder='_',
                key='chat_id'
            )

            st.session_state.messages = conversations[st.session_state.id_chat] if st.session_state.id_chat != 'New Conversation' else []
            
            def update_selectbox_remove(conversations_path, conversations):
                conversations.pop(st.session_state.chat_id)
                with open(conversations_path, 'wb') as fp:
                    packed_bytes = msgpack.packb(conversations, use_bin_type=True)
                    fp.write(packed_bytes)
                st.session_state.chat_id = 'New Conversation'
                    
            
            st.button(
                'Delete Conversation', 
                use_container_width=True, 
                disabled=False if st.session_state.id_chat != 'New Conversation' else True,
                on_click=update_selectbox_remove,
                args=(conversations_path, conversations)
            )

        @outlines.prompt
        def build_prompt_conv(user_input):
            """Summarize in one single sentence what is the subject of the conversation and what is the user talking about.

            Subject : {{ user_input }}
            """

        def generate_conv_title(llm):
            if st.session_state.chat_id == 'New Conversation':
                gen_conv = outlines.generate.text(llm)
                prompt = build_prompt_conv(st.session_state.user_input)
                output = gen_conv(
                    prompt,
                    max_tokens=15,
                    sampling_params=SamplingParams(temperature=0.3)
                )
                print(f'OUTPUT : {output}')
                st.session_state.chat_id = output
                st.session_state.messages = []
                
                conversations.update({st.session_state.chat_id: st.session_state.messages})
                with open(conversations_path, 'wb') as fp:
                    packed_bytes = msgpack.packb(conversations, use_bin_type=True)
                    fp.write(packed_bytes)
                    
        for message in st.session_state.messages:
            with st.chat_message(message["role"]):
                st.markdown(message["content"])     
                
        if prompt := st.chat_input(
            "Message Video Game Assistant", 
            on_submit=generate_conv_title, 
            key='user_input',
            args=(llm,)
        ):
            st.chat_message("user").markdown(prompt)
            st.session_state.messages.append({"role": "user", "content": prompt})
            
            ai_response = main(prompt, client, collection_name, llm, dense_model, sparse_model, "\n".join([f'{msg["role"]}: {msg["content"]}' for msg in st.session_state.messages]))
            with st.chat_message("assistant"):
                message_placeholder = st.empty()
                full_response = ""
                for chunk in re.split(r'(\s+)', ai_response):
                    full_response += chunk + " "
                    time.sleep(0.05)
                    message_placeholder.write(full_response + '▌')
                message_placeholder.write(re.sub('▌', '', full_response))
                                          
            st.session_state.messages.append({"role": "assistant", "content": full_response})
                
            conversations.update({st.session_state.id_chat: st.session_state.messages})
            with open(conversations_path, 'wb') as fp:
                packed_bytes = msgpack.packb(conversations, use_bin_type=True)
                fp.write(packed_bytes)

        if "cached_files" not in st.session_state:
            st.session_state.cached_files = []

        with st.sidebar:
            st.divider()
            
            if 'toggle_docs' not in st.session_state:
                st.session_state.toggle_docs = {
                    'tooltip': 'The AI answer your questions only considering the documents provided',
                    'display': True
                }
                
            st.toggle(
                label="""Enable 'Documents-Only' Mode""", 
                value=st.session_state.toggle_docs['display'],
                on_change=on_change_documents_only, 
                key="documents_only",
                help=st.session_state.toggle_docs['tooltip']
            )

            st.divider()
            
            url = st.text_input("Scrape an URL link :")
            if validators.url(url):
                docs = WebBaseLoader(url).load()
                print(f'WebBaseLoader: {docs[0].metadata}')
                
                texts, metadatas = [], []
                for doc in docs:
                    texts.append(doc.page_content)
                    del doc.metadata['title']
                    del doc.metadata['description']
                    del doc.metadata['language']
                    metadatas.append(doc.metadata)

                payload_docs, dense_embeddings, sparse_embeddings = chunk_documents(texts, metadatas, dense_model, sparse_model)
        
                client.upsert(
                    collection_name,
                    make_points(
                        texts,
                        metadatas,
                        dense_embeddings, 
                        sparse_embeddings
                    )
                )
                
                st.toast('URL Content Ingested !', icon='🎉')
            
            st.divider()
        
            uploaded_files = st.file_uploader("Upload a file :", accept_multiple_files=True, type=['pptx', 'ppt'])
            
            for uploaded_file in uploaded_files:

                if uploaded_file not in st.session_state.cached_files:
                    st.session_state.cached_files.append(uploaded_file)
                    
                    file_name = os.path.basename(uploaded_file.name)
                    base_name, ext = os.path.splitext(file_name)
                
                    processing_time = datetime.now().strftime('%d %b %Y, %I:%M %p')

                    full_path = os.path.realpath(uploaded_file.name)
                    file_type = ext.lstrip('.')
                    
                    d = {
                        'icon': icon_to_types[file_type][0],
                        'document': base_name,
                        'type': icon_to_types[file_type][1],
                        'path': full_path,
                        'time': [datetime.strptime(processing_time, '%d %b %Y, %I:%M %p')],
                        'toggle': True
                    }
                    
                    if (st.session_state.df.empty) or (base_name not in st.session_state.df['document'].tolist()):
                        st.session_state.df = pd.concat(
                            [
                                st.session_state.df, 
                                pd.DataFrame(data={
                                    'icon': icon_to_types[file_type][0],
                                    'document': base_name,
                                    'type': icon_to_types[file_type][1],
                                    'path': full_path,
                                    'time': [datetime.strptime(processing_time, '%d %b %Y, %I:%M %p')],
                                    'toggle': True
                                })
                            ],
                            ignore_index=True
                        )
                    else:
                        idx = st.session_state.df.index[st.session_state.df['document']==base_name].tolist()[0]
                        st.session_state.df.loc[idx] = {
                            'icon': icon_to_types[file_type][0],
                            'document': base_name,
                            'type': icon_to_types[file_type][1],
                            'path': full_path,
                            'time': datetime.strptime(processing_time, '%d %b %Y, %I:%M %p'),
                            'toggle': True
                        }
                    
                    st.session_state.df.to_parquet(
                        os.path.join(
                            data_editor_path, 
                            'data_editor.parquet.sz'
                        ), 
                        compression='snappy', 
                        engine='pyarrow'
                    )
                    
                    weakDict, tables = ppt_chunk(uploaded_file, nlp)
                    documents = weakDict.all_texts()

                    dense = dense_model.embed_documents(documents)
                    sparse = [s for s in sparse_model.embed(documents, 32)]

                    embeddings_path = os.path.join(os.getenv('HF_HOME'), 'embeddings')

                    def generate_unique_id(existing_ids):
                        while True:
                            new_id = uuid.uuid4()
                            while new_id.hex[0] == '0':
                                new_id = uuid.uuid4()
                            new_id = new_id.int
                            if new_id not in existing_ids:
                                return new_id

                    ids = weakDict.all_ids()

                    for filename in os.listdir(embeddings_path):
                        if '_ids' in filename:
                            list_ids = np.load(os.path.join(embeddings_path, filename), allow_pickle=True).tolist()

                            for i, ids_ in enumerate(ids):
                                if ids_ in list_ids:
                                    ids[i] = generate_unique_id(list_ids)

                    payload_docs = [{ 'text': documents[i], 'metadata': metadata } for i, metadata in enumerate(weakDict.all_metadatas())]

                    print(f'LEN : {len(ids)}, {len(payload_docs)}, {len(dense)}, {len([SparseVector(indices=s.indices.tolist(), values=s.values.tolist()) for s in sparse])}')
                    
                    client.upsert(
                        collection_name=collection_name,
                        points=Batch(
                            ids=ids,
                            payloads=payload_docs,
                            vectors={
                                'text-dense': dense,
                                'text-sparse': [SparseVector(indices=s.indices.tolist(), values=s.values.tolist()) for s in sparse]
                            }
                        )
                    )

                    payload_path = os.path.join(embeddings_path, base_name + '_payload.msgpack')
                    dense_path = os.path.join(embeddings_path, base_name + '_dense.npz')
                    sparse_path = os.path.join(embeddings_path, base_name + '_sparse.npz')
                    ids_path = os.path.join(embeddings_path, base_name + '_ids.npy')

                    with open(payload_path, "wb") as outfile_texts:
                        packed_payload = msgpack.packb(payload_docs, use_bin_type=True)
                        outfile_texts.write(packed_payload)
            
                    np.savez_compressed(dense_path, *dense)
                    max_index = 0
                    for embedding in sparse:
                        if len(embedding.indices) > 0:
                            max_index = max(max_index, max(embedding.indices))
        
                    sparse_matrices = []
                    
                    for embedding in sparse:
                        data = np.array(embedding.values)
                        indices = np.array(embedding.indices)
                        indptr = np.array([0, len(data)])
                        matrix = csr_matrix((data, indices, indptr), shape=(1, max_index + 1))
                        sparse_matrices.append(matrix)
            
                    combined_sparse_matrix = vstack(sparse_matrices)
                    save_npz(sparse_path, combined_sparse_matrix)

                    insert_data(conn, cursor, base_name, ids)
                    np.save(ids_path, np.array(ids), allow_pickle=True)

                    st.toast('Document(s) Ingested !', icon='🎉')